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Xingjian Shi
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2020 – today
- 2024
- [c29]Boran Han, Shuai Zhang, Xingjian Shi, Markus Reichstein:
Bridging Remote Sensors with Multisensor Geospatial Foundation Models. CVPR 2024: 27852-27862 - [i30]Boran Han, Shuai Zhang, Xingjian Shi, Markus Reichstein:
Bridging Remote Sensors with Multisensor Geospatial Foundation Models. CoRR abs/2404.01260 (2024) - [i29]Hao Wu, Xingjian Shi, Ziyue Huang, Penghao Zhao, Wei Xiong, Jinbao Xue, Yangyu Tao, Xiaomeng Huang, Weiyan Wang:
BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics. CoRR abs/2405.17051 (2024) - 2023
- [c28]Yuxin Ren, Zihan Zhong, Xingjian Shi, Yi Zhu, Chun Yuan, Mu Li:
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation. ACL (1) 2023: 1990-2006 - [c27]Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson:
Automated Few-Shot Classification with Instruction-Finetuned Language Models. EMNLP (Findings) 2023: 2414-2432 - [c26]Matías Mendieta, Boran Han, Xingjian Shi, Yi Zhu, Chen Chen:
Towards Geospatial Foundation Models via Continual Pretraining. ICCV 2023: 16760-16770 - [c25]Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, Diyi Yang:
Parameter-Efficient Fine-Tuning Design Spaces. ICLR 2023 - [c24]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. ICLR 2023 - [c23]Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran:
XTab: Cross-table Pretraining for Tabular Transformers. ICML 2023: 43181-43204 - [c22]Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Yi Zhu, Mu Li, Yuyang Wang:
PreDiff: Precipitation Nowcasting with Latent Diffusion Models. NeurIPS 2023 - [c21]Wenting Ye
, Hongfei Yang
A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data. SIGIR 2023: 3325-3329 - [i28]Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, Diyi Yang:
Parameter-Efficient Fine-Tuning Design Spaces. CoRR abs/2301.01821 (2023) - [i27]Matías Mendieta, Boran Han, Xingjian Shi, Yi Zhu, Chen Chen, Mu Li:
GFM: Building Geospatial Foundation Models via Continual Pretraining. CoRR abs/2302.04476 (2023) - [i26]Jiaxin Cheng, Xiao Liang, Xingjian Shi, Tong He, Tianjun Xiao, Mu Li:
LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation. CoRR abs/2302.08908 (2023) - [i25]Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran:
XTab: Cross-table Pretraining for Tabular Transformers. CoRR abs/2305.06090 (2023) - [i24]Yuxin Ren, Zihan Zhong, Xingjian Shi, Yi Zhu, Chun Yuan, Mu Li:
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation. CoRR abs/2305.09651 (2023) - [i23]Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson:
Automated Few-shot Classification with Instruction-Finetuned Language Models. CoRR abs/2305.12576 (2023) - [i22]Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Yi Zhu, Mu Li, Yuyang Wang:
PreDiff: Precipitation Nowcasting with Latent Diffusion Models. CoRR abs/2307.10422 (2023) - 2022
- [c20]Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang:
Removing Batch Normalization Boosts Adversarial Training. ICML 2022: 23433-23445 - [c19]Nick Erickson, Xingjian Shi, James Sharpnack, Alexander J. Smola:
Multimodal AutoML for Image, Text and Tabular Data. KDD 2022: 4786-4787 - [c18]Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-Yan Yeung:
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. NeurIPS 2022 - [i21]Yaojie Hu, Xingjian Shi, Qiang Zhou, Lee Pike:
Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar. CoRR abs/2204.06643 (2022) - [i20]Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang:
Removing Batch Normalization Boosts Adversarial Training. CoRR abs/2207.01156 (2022) - [i19]Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-Yan Yeung:
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. CoRR abs/2207.05833 (2022) - [i18]Yunhe Gao, Xingjian Shi, Yi Zhu, Hao Wang, Zhiqiang Tang, Xiong Zhou, Mu Li, Dimitris N. Metaxas:
Visual Prompt Tuning for Test-time Domain Adaptation. CoRR abs/2210.04831 (2022) - [i17]Wenting Ye
A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data. CoRR abs/2211.02533 (2022) - [i16]Jielin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao
Are Multimodal Models Robust to Image and Text Perturbations? CoRR abs/2212.08044 (2022) - [i15]Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang:
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting. CoRR abs/2212.08151 (2022) - [i14]M. Saiful Bari
SPT: Semi-Parametric Prompt Tuning for Multitask Prompted Learning. CoRR abs/2212.10929 (2022) - [i13]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. CoRR abs/2212.14453 (2022) - 2021
- [c17]Aashiq Muhamed, Liang Li, Xingjian Shi, Suri Yaddanapudi, Wayne Chi, Dylan Jackson, Rahul Suresh, Zachary C. Lipton, Alexander J. Smola:
Symbolic Music Generation with Transformer-GANs. AAAI 2021: 408-417 - [c16]Cody Hao Yu, Xingjian Shi, Haichen Shen, Zhi Chen, Mu Li, Yida Wang:
Lorien: Efficient Deep Learning Workloads Delivery. SoCC 2021: 18-32 - [c15]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. SustaiNLP@EMNLP 2021: 119-133 - [c14]Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola:
Benchmarking Multimodal AutoML for Tabular Data with Text Fields. NeurIPS Datasets and Benchmarks 2021 - [i12]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. CoRR abs/2109.11105 (2021) - [i11]Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola:
Benchmarking Multimodal AutoML for Tabular Data with Text Fields. CoRR abs/2111.02705 (2021) - 2020
- [j1]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu:
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. J. Mach. Learn. Res. 21: 23:1-23:7 (2020) - [c13]Jonas Mueller, Xingjian Shi, Alexander J. Smola:
Faster, Simpler, More Accurate: Practical Automated Machine Learning with Tabular, Text, and Image Data. KDD 2020: 3509-3510
2010 – 2019
- 2019
- [c12]Haibin Lin, Xingjian Shi, Leonard Lausen, Aston Zhang, He He, Sheng Zha, Alexander J. Smola:
Dive into Deep Learning for Natural Language Processing. EMNLP/IJCNLP (2) 2019 - [c11]Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. IJCAI 2019: 4264-4270 - [i10]Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King:
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. CoRR abs/1905.13129 (2019) - [i9]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. CoRR abs/1907.04433 (2019) - 2018
- [c10]Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung:
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. UAI 2018: 339-349 - [i8]Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung:
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. CoRR abs/1803.07294 (2018) - [i7]Xingjian Shi, Dit-Yan Yeung:
Machine Learning for Spatiotemporal Sequence Forecasting: A Survey. CoRR abs/1808.06865 (2018) - 2017
- [c9]Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Relational Deep Learning: A Deep Latent Variable Model for Link Prediction. AAAI 2017: 2688-2694 - [c8]Feng Xiong, Xingjian Shi, Dit-Yan Yeung:
Spatiotemporal Modeling for Crowd Counting in Videos. ICCV 2017: 5161-5169 - [c7]Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, Wang-chun Woo:
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. NIPS 2017: 5617-5627 - [c6]Jiani Zhang, Xingjian Shi, Irwin King
Dynamic Key-Value Memory Networks for Knowledge Tracing. WWW 2017: 765-774 - [i6]Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, Wang-chun Woo:
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. CoRR abs/1706.03458 (2017) - [i5]Feng Xiong, Xingjian Shi, Dit-Yan Yeung:
Spatiotemporal Modeling for Crowd Counting in Videos. CoRR abs/1707.07890 (2017) - 2016
- [c5]Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Natural-Parameter Networks: A Class of Probabilistic Neural Networks. NIPS 2016: 118-126 - [c4]Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks. NIPS 2016: 415-423 - [i4]Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Natural-Parameter Networks: A Class of Probabilistic Neural Networks. CoRR abs/1611.00448 (2016) - [i3]Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks. CoRR abs/1611.00454 (2016) - [i2]Jiani Zhang, Xingjian Shi, Irwin King, Dit-Yan Yeung:
Dynamic Key-Value Memory Network for Knowledge Tracing. CoRR abs/1611.08108 (2016) - 2015
- [c3]Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Relational Stacked Denoising Autoencoder for Tag Recommendation. AAAI 2015: 3052-3058 - [c2]Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, Wang-chun Woo:
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. NIPS 2015: 802-810 - [i1]Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, Wang-chun Woo:
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. CoRR abs/1506.04214 (2015) - 2014
- [c1]Long Qian, Xingjian Shi:
Denoising predictive sparse decomposition. BigComp 2014: 223-228
Coauthor Index
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last updated on 2024-10-08 20:34 CEST by the dblp team
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